






"""simple docstring"""
import json
import os
import unittest

from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES, BioGptTokenizer
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin



class        UpperCamelCase__ (				a_							,	unittest.TestCase):







    """simple docstring"""
    __UpperCAmelCase           = BioGptTokenizer
    __UpperCAmelCase           = False







    def 				a__			(		self :	Any ):





           '''simple docstring'''

           super().setUp()

           # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
           __magic_name__      				=    [
               'l',
               'o',
               'w',
               'e',
               'r',
               's',
               't',
               'i',
               'd',
               'n',
               'w</w>',
               'r</w>',
               't</w>',
               'lo',
               'low',
               'er</w>',
               'low</w>',
               'lowest</w>',
               'newer</w>',
               'wider</w>',
               '<unk>',
           ]
           __magic_name__      				=    dict(zip(UpperCamelCase_				,    range(len(UpperCamelCase_ ) ) ) )
           __magic_name__      				=    ['l o 123', 'lo w 1456', 'e r</w> 1789', '']

           __magic_name__      				=    os.path.join(self.tmpdirname				,    VOCAB_FILES_NAMES['vocab_file'] )
           __magic_name__      				=    os.path.join(self.tmpdirname				,    VOCAB_FILES_NAMES['merges_file'] )
           with open(self.vocab_file				,    'w' ) as fp:
                  fp.write(json.dumps(UpperCamelCase_ ) )
           with open(self.merges_file				,    'w' ) as fp:
                  fp.write('\n'.join(UpperCamelCase_ ) )







    def 				a__			(		self :	Union[str, Any]				,    UpperCamelCase_ :	Dict ):





           '''simple docstring'''

           __magic_name__      				=    'lower newer'
           __magic_name__      				=    'lower newer'
           return input_text, output_text







    def 				a__			(		self :	List[Any] ):





           '''simple docstring'''

           __magic_name__      				=    BioGptTokenizer(self.vocab_file				,    self.merges_file )

           __magic_name__      				=    'lower'
           __magic_name__      				=    ['low', 'er</w>']
           __magic_name__      				=    tokenizer.tokenize(UpperCamelCase_ )
           self.assertListEqual(UpperCamelCase_				,    UpperCamelCase_ )

           __magic_name__      				=    tokens + ['<unk>']
           __magic_name__      				=    [1_4, 1_5, 2_0]
           self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCamelCase_ )				,    UpperCamelCase_ )







    @slow
    def 				a__			(		self :	Any ):





           '''simple docstring'''

           __magic_name__      				=    BioGptTokenizer.from_pretrained('microsoft/biogpt' )

           __magic_name__      				=    tokenizer.encode('sequence builders'				,    add_special_tokens=UpperCamelCase_ )
           __magic_name__      				=    tokenizer.encode('multi-sequence build'				,    add_special_tokens=UpperCamelCase_ )

           __magic_name__      				=    tokenizer.build_inputs_with_special_tokens(UpperCamelCase_ )
           __magic_name__      				=    tokenizer.build_inputs_with_special_tokens(UpperCamelCase_				,    UpperCamelCase_ )

           self.assertTrue(encoded_sentence == [2] + text )
           self.assertTrue(encoded_pair == [2] + text + [2] + text_a )







"""simple docstring"""
import argparse
import json
import os

from tensorflow.core.protobuf.saved_model_pba import SavedModel


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
snake_case : Optional[Any]     		=		"""."""

# Internal TensorFlow ops that can be safely ignored (mostly specific to a saved model)
snake_case : List[str]     		=		[
    """Assert""",
    """AssignVariableOp""",
    """EmptyTensorList""",
    """MergeV2Checkpoints""",
    """ReadVariableOp""",
    """ResourceGather""",
    """RestoreV2""",
    """SaveV2""",
    """ShardedFilename""",
    """StatefulPartitionedCall""",
    """StaticRegexFullMatch""",
    """VarHandleOp""",
]


def 							A						(				__snake_case: Optional[Any] ,     __snake_case: Tuple ,     __snake_case: Any  )    ->		int:

       """simple docstring"""





       __magic_name__      				=    SavedModel()
       __magic_name__      				=    []

       with open(os.path.join(__snake_case ,     'utils' ,     'tf_ops' ,     'onnx.json'  )  ) as f:
              __magic_name__      				=    json.load(__snake_case  )['opsets']

       for i in range(1 ,     opset + 1  ):
              onnx_ops.extend(onnx_opsets[str(__snake_case  )]  )

       with open(__snake_case ,     'rb'  ) as f:
              saved_model.ParseFromString(f.read()  )

       __magic_name__      				=    set()

       # Iterate over every metagraph in case there is more than one (a saved model can contain multiple graphs)
       for meta_graph in saved_model.meta_graphs:
              # Add operations in the graph definition
              model_op_names.update(node.op for node in meta_graph.graph_def.node  )

              # Go through the functions in the graph definition
              for func in meta_graph.graph_def.library.function:
                     # Add operations in each function
                     model_op_names.update(node.op for node in func.node_def  )

    # Convert to list, sorted if you want
       __magic_name__      				=    sorted(__snake_case  )
       __magic_name__      				=    []

       for op in model_op_names:
              if op not in onnx_ops and op not in INTERNAL_OPS:
                     incompatible_ops.append(__snake_case  )

       if strict and len(__snake_case  ) > 0:
              raise Exception(F"""Found the following incompatible ops for the opset {opset}:\n""" + incompatible_ops  )
       elif len(__snake_case  ) > 0:
              print(F"""Found the following incompatible ops for the opset {opset}:"""  )
              print(*__snake_case ,     sep='\n'  )
       else:
              print(F"""The saved model {saved_model_path} can properly be converted with ONNX."""  )


if __name__ == "__main__":
 snake_case : Optional[Any]     		=		argparse.ArgumentParser()
 parser.add_argument("""--saved_model_path""", help="""Path of the saved model to check (the .pb file).""")
 parser.add_argument(
     """--opset""", default=1_2, type=int, help="""The ONNX opset against which the model has to be tested."""
 )
 parser.add_argument(
     """--framework""", choices=["""onnx"""], default="""onnx""", help="""Frameworks against which to test the saved model."""
 )
 parser.add_argument(
     """--strict""", action="""store_true""", help="""Whether make the checking strict (raise errors) or not (raise warnings)"""
 )
 snake_case : Any     		=		parser.parse_args()

 if args.framework == "onnx":
  onnx_compliancy(args.saved_model_path, args.strict, args.opset)